四网融合对市民出行成本影响分析
于展1张晓宾2钱泽林2孙双篪2
Analysis of Four-network Integration Impact on Citizens' Travel Costs
YU Zhan1ZHANG Xiaobin2QIAN Zelin2SUN Shuangchi2
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作者信息:1.中铁上海设计院集团有限公司，200070，上海
2.上海申铁投资有限公司，200031，上海
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Affiliation:1.China Railway Shanghai Design Institute Group Co.， Ltd. 200070， Shanghai， China
2.Shanghai Shen-Tie Investment Co.， Ltd.， 200031， Shanghai， China
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Key words:
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DOI:10.16037/j.1007-869x.20252143
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中图分类号/CLCN:U293
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栏目/Col:规划与投融资
摘要:
［目的］ 四网融合通过整合轨道交通路网资源，成为优化城市交通结构、降低出行成本的关键路径，为此有必要开展四网融合的出行成本分析。［方法］ 以上海市为研究对象，构建基于XGBoost算法的出行选择模型，结合SHAP方法解析出行决策影响因素，明确四网融合对居民出行选择的作用机制，量化对居民出行的影响；通过1 km×1 km栅格化人口统计、多源交通数据融合及机器学习建模，选择出行总时间、平峰时间、费用、在途时间及体力消耗为关键影响因素。［结果及结论］ 上海市虹桥枢纽实证结果表明，四网融合可提升人均出行效率20.7%，在强化枢纽辐射能级、优化城市群出行结构中发挥着关键作用。研究结果验证了XGBoost算法在四网融合政策效应评估中的有效性，可为轨道交通四网融合政策制定与优化提供理论依据与实证支撑。
Abstracts:
［Objective］ The integration of mainline railway， intercity railway， suburban railway and urban rail transit networks （abbreviated as four-network integration） has become a key strategy for optimizing urban transport structure and reducing travel costs through integrating rail transit and road network resources. Therefore， it is necessary to conduct an analysis about the four-network integration travel costs. ［Method］ Taking Shanghai as the research object， a travel choice model based on XGBoost is constructed， and the SHAP （Shapley additive explanations） method is applied to interpret the influencing factors in travel decisions. The mechanism of four-network integration on residents' travel choices is systematically analyzed， and its impact on travel behavior is quantitatively assessed. Through a combination of 1 km×1 km gridded demographic data， multi-source transportation data fusion， and machine learning modeling， the total travel time， off-peak travel time， cost， in-transit time， and physical exertion are selected as key influencing factors. ［Result Conclusion］ An empirical study at Shanghai Hongqiao Hub shows that four-network integration can improve per capita travel efficiency by 20.7%， playing a key role in enhancing the hub's regional influence and optimizing the travel structure of urban agglomeration. The effectiveness of the XGBoost machine learning framework in assessing the policy impacts of four-network integration is demonstrated， providing theoretical and empirical support for policy formulation and optimization in rail transit four-network integration.
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